Shaojun Guo
Biographic Data
| ID | 8920459 |
|---|---|
| NAME | Shaojun Guo |
| GIVEN NAMES | Shaojun |
| FAMILY NAME | Guo |
| SIGNATURE | GUO S |
| AFFILIATIONS | Renmin University of China |
| ORCID | 0000-0003-4262-4690 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Factor Modeling for High-Dimensional Functional Time Series
Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables p diverges as the number of serially dependent observations n increases. In this paper, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent facto…
Functional Linear Regression
Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an underlying stochastic process, and are observed over a grid of points contaminated by iid measurement errors. In practice, however, the dynamical dependence across different curves may exist and the parametric assumption on the error covariance structure could be unreali…
No prominent works on this page.
Functional Linear Regression
Functional linear regression is an important topic in functional data analysis. It is commonly assumed that samples of the functional predictor are independent realizations of an underlying stochastic process, and are observed over a grid of points contaminated by iid measurement errors. In practice, however, the dynamical dependence across different curves may exist and the parametric assumption on the error covariance structure could be unreali…
Factor Modeling for High-Dimensional Functional Time Series
Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables p diverges as the number of serially dependent observations n increases. In this paper, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent facto…
Statistical Methods and Inference (2 works) · Advanced Statistical Methods and Models (1 work) · Advanced Statistical Process Monitoring (1 work) · Applied Mathematics (1 work) · Autocovariance (1 work) · Biology (1 work) · Complex Systems and Time Series Analysis (1 work) · Computer Science (1 work) · Covariance (1 work) · Econometrics (1 work)